Biomimetic Hydrogel‐Mediated Mechano‐Immunometabolic Therapy for Inhibition of ccRCC Recurrence After Surgery

Abstract The unique physical tumor microenvironment (TME) and aberrant immune metabolic status are two obstacles that must be overcome in cancer immunotherapy to improve clinical outcomes. Here, an in situ mechano‐immunometabolic therapy involving the injection of a biomimetic hydrogel is presented with sequential release of the anti‐fibrotic agent pirfenidone, which softens the stiff extracellular matrix, and small interfering RNA IDO1, which disrupts kynurenine‐mediated immunosuppressive metabolic pathways, together with the multi‐kinase inhibitor sorafenib, which induces immunogenic cell death. This combination synergistically augmented tumor immunogenicity and induced anti‐tumor immunity. In mouse models of clear cell renal cell carcinoma, a single‐dose peritumoral injection of a biomimetic hydrogel facilitated the perioperative TME toward a more immunostimulatory landscape, which prevented tumor relapse post‐surgery and prolonged mouse survival. Additionally, the systemic anti‐tumor surveillance effect induced by local treatment decreased lung metastasis by inhibiting epithelial‐mesenchymal transition conversion. The versatile localized mechano‐immunometabolic therapy can serve as a universal strategy for conferring efficient tumoricidal immunity in “cold” tumor postoperative interventions.


Figure S3
. Gene-silencing efficiency was quantified using RT-qPCR.Among the IDO1 siRNA synthesized by the three primers provided, the one with the best silencing effect was chosen for subsequent experiment. Primers: Data were expressed as means ± SD (n = 3).Statistical differences were calculated using a two-tailed unpaired Student's t-test.*P < 0.05.3).Statistical differences were calculated using a two-tailed unpaired Student's t-test.

Figure S1 .
Figure S1.TGF-β1 expression in tumor tissues of ccRCC patients.A) A boxplot of TGF-β1 expression in ccRCC patients (n = 523) and healthy control (n = 100) isolated from TCGA and Genotype-Tissue Expression determined by gene expression profiling interactive analysis.The data were transformed as log2 (TPM + 1) and n is the number of biologically independent samples.B) Survival curves of ccRCC patients with high (n = 400) and low (n = 400) TGF-β1 expression.Statistical differences were calculated using a two-tailed unpaired Student's t-test.The survival analyses were performed by the log-rank (Mantel-Cox) test.*P < 0.05.

Figure S2 .
Figure S2.IDO1 expression in renal tumor tissues of ccRCC patients.A) A boxplot of IDO1 expression in ccRCC patients (n = 523) and healthy people (n = 100) isolated from TCGA and Genotype-Tissue Expression determined by gene expression profiling interactive analysis.The data were transformed as log2 (TPM + 1) and n is the number of biologically independent samples.B) Survival curves of ccRCC patients with high (n = 400) and low (n = 400) IDO1 expression.C) Western blotting and relative quantification of IDO1 protein in ccRCC patients.Data were expressed as means ± SD (n = 3).Statistical differences were calculated using a two-tailed

Figure S4 .
Figure S4.SDS-PAGE assay of CD62E protein in CISE NPs.SDS-PAGE protein analysis to reveal that the CD62E protein was anchored on the surface of the CISE NPs.Samples were stained with coomassie brilliant blue dye solution and visualized by a light transilluminator and a digital imaging system.

Figure S5 .
Figure S5.The size distributions of CISE NPs in PBS or DMEM for 7 days.

Figure S6 .
Figure S6.Rheological properties analysis of the free Gel and CISE-PFD@Gel.Variation in the moduli of the hydrogel over time.G', elastic modulus; G", viscous modulus.

Figure S8 .
Figure S8.Quantification of the ICD effect triggered by CISE NPs.A) Corresponding mean fluorescence intensity of CRT exposure in Renca cells after

Figure S9 .
Figure S9.Representative digital photos of the treated mice from each group (Control, Gel and PFD@Gel).The circle represents the tumor site.

Figure S10 .
Figure S10.Quantitative analysis of α-SMA and COL1A1 in tumor tissues.A) Relative quantification of α-SMA expression within tumor sections from mice after different treatment.B) Relative quantification of COL1A1 expression within tumor sections from mice after different treatment.Data were expressed as means ± SD (n =

Figure S11 .
Figure S11.Masson's trichrome staining and relative quantification of tumor tissues after different treatment.Data were expressed as means ± SD (n = 3).Statistical differences were calculated using a two-tailed unpaired Student's t-test.ns, not significant, **P < 0.01.

Figure S13 .
Figure S13.Representative H&E staining images of tumor tissues used for AFM test.

Figure S14 .
Figure S14.Relative vessel number in tumor tissues after different treatment.Data were expressed as means ± SD (n = 3).Statistical differences were calculated using a two-tailed unpaired Student's t-test.ns, not significant.

Figure S16 .
Figure S16.Western blotting strip and relative quantification of HIF-1α protein in tumors after different treatments.Data were expressed as means ± SD (n = 3).Statistical differences were calculated using a two-tailed unpaired Student's t-test.**P < 0.01.

Figure S17 .
Figure S17.Photoacoustic images and corresponding quantification in oxygen saturation mode of tumors after different treatment (n = 3).Data were expressed as means ± SD (n = 3).Statistical differences were calculated using a two-tailed unpaired Student's t-test.*P < 0.05.

Figure S18 .
Figure S18.The CISE-PFD@Gel-augmented mechano-immunometabolic therapy against postsurgical RAG renal cell carcinoma.A) Schematic of animal experiment design.B) Residual tumor growth kinetics of mice after different treatments (n = 6).C) Weight of the excised tumors examined on day 14 after different treatments (n = 6).D) Digital photos of the excised tumors examined on day 14 after different treatments.Data were expressed as means ± SD.Statistical difference was calculated using unpaired student's t-test, *P < 0.05, ***P < 0.001.

Figure S19 .
Figure S19.The CISE-PFD@Gel-augmented mechano-immunometabolic therapy against postsurgical B16-F10 melanoma.A) Schematic illustration of the study design to assess the CISE-PFD@Gel treatment efficacy in B16-F10 melanoma.B) Residual tumor growth kinetics of mice after different treatments (n = 6).C) Weight of the excised tumors examined on day 15 after different treatments (n = 6).D) Digital photos of the excised tumors examined on day 15 after different treatments.SR, surgical resection.Data were expressed as means ± SD.Statistical difference was calculated using unpaired student's t-test, ***P < 0.001.

Figure S23 .
Figure S23.Regulatory effects of IDO1 downstream signaling.A) The ratio of Kyn versus Trp in tumor tissues was determined by HPLC.B) Relative quantification of AhR protein within tumor tissues after different treatments.Data were expressed as means ± SD (n = 3).Statistical differences were calculated using a two-tailed unpaired Student's t-test.*P < 0.05, **P < 0.01 and ***P < 0.001.G1, Control; G2,

Figure S24 .
Figure S24.Relative quantification of CRT and HMGB1 proteins within tumor tissues after different treatments.Data were expressed as means ± SD (n = 3).

Figure S25 .
Figure S25.Infiltration of TAM subtypes within tumors.A) Representative flow cytometric images and C) relative quantification of M1-TAMs.B) Representative flow cytometric images and D) relative quantification of M2-TAMs.E) The ratio of M1-TAMs versus M2-TAMs.Data were expressed as means ± SD (n = 3).Statistical differences were calculated using a two-tailed unpaired Student's t-test.*P < 0.05,

Figure S26 .
Figure S26.Gating strategy for flow cytometry analysis of immune cells.A) Gating strategy for isolating CD80 + CD86 + mDCs from tumor tissues.B) Gating strategy for isolating T cell subsets.C) Gating strategy for isolating TAMs from tumor tissues.